Multi-model intercomparison of the pan-Arctic ice-algal productivityon seasonal, interannual, and decadal timescales

Eiji Watanabe, JAMSTEC Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan, Meibing Jin, University of Alaska Fairbanks, Fairbanks, AK, United States, Hakase Hayashida, University of Victoria, Victoria, BC, Canada, Jinlun Zhang, University of Washington, Seattle, WA, United States and Nadja Steiner, CCCma, Victoria, BC, Canada
Abstract:
Seasonal, interannual, and decadal variations in the Arctic ice-algal productivity for 1980–2009 are investigated using daily outputs from five sea ice‒ocean ecosystem models participating in the Forum for Arctic Modeling and Observational Synthesis (FAMOS) project. The five FAMOS models all show a shelf‒basin contrast in the simulated spatial distribution of ice-algal productivity. The higher productivity is simulated in the Arctic shelf due to a combination of thinner sea ice and nutrient-rich conditions relative to those in the central basin. The FAMOS models qualitatively reproduce seasonal cycles of snow, sea-ice, and ocean properties related to ice algae for four sub-regions: Chukchi Sea, Canada Basin, Eurasian Basin, and Barents Sea. These properties substantially vary among the sub-regions and among the five models, respectively. The simulated annual total ice-algal productivity has no common decadal trend at least for 1980‒2009 among the five models in any of the four sub-regions, although the simulated snow depth and sea-ice thickness in spring are mostly declining. The model intercomparison indicates that an appropriate balance of stable ice-algal habitat (i.e., sea-ice cover) and enough light availability is necessary to retain the productivity. The selected value for the maximum growth rate of the ice-algal photosynthesis term is a key source for the inter-model spreads. Understanding of the simulated uncertainties on the pan-Arctic and decadal scales are expected to improve coupled sea ice‒ocean ecosystem models. This step will be a baseline for further modeling/field studies and future projections.